(2003) are among the first to study SVM and text mining methods in the market prediction domain, where they align financial news articles with multiple time series to simulate the 33 stocks in the Hong Kong Hang Seng Index.

However, by using an SVM ranker to combine the realizer’s model score together with features from multiple parsers, including ones designed to make the ranker more robust to parsing mistakes, we show that significant increases in BLEU scores can be achieved.

Abstract

Moreover, via a targeted manual analysis, we demonstrate that the SVM reranker frequently manages to avoid vicious ambiguities, while its ranking errors tend to affect fluency much more often than adequacy.

Introduction

Consequently, we examine two reranking strategies, one a simple baseline approach and the other using an SVM reranker (J oachims, 2002).

Introduction

Therefore, to develop a more nuanced self-monitoring reranker that is more robust to such parsing mistakes, we trained an SVM using dependency precision and recall features for all three parses, their n-best parsing results, and per-label precision and recall for each type of dependency, together with the realizer’s normalized perceptron model score as a feature.

Introduction

With the SVM reranker, we obtain a significant improvement in BLEU scores over

Reranking with SVMs 4.1 Methods

Similarly, we conjectured that large differences in the realizer’s perceptron model score may more reliably reflect human fluency preferences than small ones, and thus we combined this score with features for parser accuracy in an SVM ranker.

Reranking with SVMs 4.1 Methods

Additionally, given that parsers may more reliably recover some kinds of dependencies than others, we included features for each dependency type, so that the SVM ranker might learn how to weight them appropriately.

Reranking with SVMs 4.1 Methods

We trained the SVM ranker (J oachims, 2002) with a linear kernel and chose the hyper-parameter c, which tunes the tradeoff between training error and margin, with 6-fold cross-validation on the devset.

For SVM , models trained on POS and LIWC features achieve even lower accuracy than Unigram.

Experiments

tive model, SAGE achieve much better results than SVM , and is around 0.65 accurate in the cross-domain task.

Feature-based Additive Model

If we instead use SVM , for example, we would have to train classifiers one by one (due to the distinct features from different sources) to draw conclusions regarding the differences between Turker vs Expert vs truthful reviews, positive expert vs negative expert reviews, or reviews from different domains.

Introduction

In the examples in Table l, we trained a linear SVM classifier on Ott’s Chicago-hotel dataset on unigram features and tested it on a couple of different domains (the details of data acquisition are illustrated in Section 3).

Introduction

Table 1: SVM performance on datasets for a classifier trained on Chicago hotel review based on Unigram feature.

Methods: We evaluated the overall performance relative to the common SVM bag of words approach that can be ubiquitously found in text mining literature.

Experiments

o SVM-TF: Uses a bag of words SVM with term frequency weights.

Experiments

SVM-Delta-IDF: Uses a bag of words SVM classification with TF.Delta-IDF weights (Formula 2) in the feature vectors before training or testing an SVM .

Related Work

(2012) propose an algorithm which first trains individual SVM classifiers on several small, class-balanced, random subsets of the dataset, and then reclassifies each training instance using a majority vote of these individual classifiers.

Table 4 shows that of the highest-weighted SVM features learned when training models for HOW questions on YA and Bio, many are shared (e.g., 56.5% of the features in the top half of both DPMs are shared), suggesting that a core set of discourse features may be of utility across domains.

CR + LS + DMM + DPM 39.32* +24% 47.86* +20%

Table 4: Percentage of top features with the highest SVM weights that are shared between Bio HOW and YA models.

All we need to employ the structured perceptron algorithm (Collins, 2002) or the structured SVM algorithm (Tsochantaridis et al., 2004) is a black-box procedure for performing MAP inference in the structured linear model given an arbitrary cost vector.

Soft Constraints in Dual Decomposition

This can be ensured by simple modifications of the perceptron and subgradient descent optimization of the structured SVM objective simply by truncating c coordinate-wise to be nonnegative at every learning iteration.

An event causality candidate is given a causality score 0 8 core, which is the SVM score (distance from the hyperplane) that is normalized to [0,1] by the sigmoid function Each event causality candidate may be given multiple original sentences, since a phrase pair can appear in multiple sentences, in which case it is given more than one SVM score.

Experiments

(2011): CEAWS is an unsupervised method that uses CEA to rank event causality candidates, and CEAsup is a supervised method using SVM and the CEA features, whose ranking is based on the SVM scores.

Experiments

The baselines are as follows: Csuns is an unsupervised method that uses 03 for ranking, and Cssup is a supervised method using SVM with 03 as the only feature that uses SVM scores for ranking.

In particular, starting from EDUs, at each step of the tree-building, a binary SVM classifier is first applied to determine which pair of adjacent discourse constituents should be merged to form a larger span, and another multi-class SVM classifier is then applied to assign the type of discourse relation that holds between the chosen pair.

Related work

Also, the employment of SVM classifiers allows the incorporation of rich features for better data representation (Feng and Hirst, 2012).

Related work

However, HILDA’s approach also has obvious weakness: the greedy algorithm may lead to poor performance due to local optima, and more importantly, the SVM classifiers are not well-suited for solving structural problems due to the difficulty of taking context into account.